Gartner Research

Hype Cycle for Data Science, 2016

Published: 25 July 2016

ID: G00303293

Analyst(s): Jim Hare , Alexander Linden , Peter Krensky

Summary

Advances in data science are sparking more creative business opportunities. While much of the hype is for artificial intelligence and deep learning, this Hype Cycle shows the breadth and depth of excitement about data science, with new technologies and some significant movements from last year.

Table Of Contents

Analysis

  • What You Need to Know
  • The Hype Cycle
  • The Priority Matrix
  • Off the Hype Cycle
  • On the Rise
    • Probability Management
    • Guided Analytics
    • Algorithm Marketplaces
    • Deep Reinforcement Learning
    • Edge Analytics
    • Model Factory
    • Notebooks
    • Advanced Anomaly Detection
    • Citizen Data Science
    • Smart Data Discovery
    • Cognitive Computing
    • Graph Analytics
  • At the Peak
    • Optimization
    • Prescriptive Analytics
    • Python
    • Deep Neural Nets
    • Event Stream Processing
    • Machine Learning
    • Self-Service Data Preparation
    • Data Lakes
    • Spark
    • Predictive Analytics
    • Hadoop-Based Data Discovery
  • Sliding Into the Trough
    • Natural-Language Question Answering
    • Model Management
    • Speech Analytics
  • Climbing the Slope
    • Text Analytics
    • Video/Image Analytics
    • Ensemble Learning
    • Simulation
  • Entering the Plateau
    • R
  • Appendixes
    • Hype Cycle Phases, Benefit Ratings and Maturity Levels

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